Warning:This page is
provided for historical and archival purposes
only. While the seminar dates are correct, we offer no
guarantee of informational accuracy or link
validity. Contact information for the speakers, hosts and
seminar committee are certainly out of date.

ABSTRACT

With modern intelligence gathering methods, such as with the AWACS
or J-STARS aircraft, it is now possible to remotely monitor, in real
time, the motion of one or more ground or air vehicles. This data
gives past and present locations of vehicles but does not in itself
indicate their future path or goal. This talk deals with the
prediction of future paths and goals of vehicles observed to be moving
over a known environment.

Intelligent path prediction addresses the problem of predicting
the path of a vehicle performing a transit mission. Such a mission
proceeds from a start location to a goal location guided by an
intelligent planning strategy (e.g., minimize distance, minimize
visibility, maximize safety, etc.). Given the history of a path from a
start location to a current location, the objectives are: 1) to estimate
the cost criterion guiding the travel, 2) to predict the goal
location (or select a goal location from a set of candidate goal
locations), and 3) to predict the future path leading to the predicted goal
location. First, a cost criterion explaining the decision-making strategy
of the observed vehicle is estimated using a correlation measure comparing
the observed path data to optimal path search information. This correlation
is expressed in terms of the tolerance epsilon of an epsilon-optimal
path. Next, a region of plausible goal locations is predicted assuming
that the vehicle will proceed using either optimal decisions
or epsilon-optimal decisions in the future. The predicted goal location
of the vehicle is determined by selecting the point in the region of
plausible goal locations that has the highest heuristic merit, as determined
by a proposed ranking system. Finally, from auxiliary search information,
the future path is predicted. This problem is generalized to predicting
the future path of a point vehicle traveling in an arbitrary dimensional
space.